Spatio-temporal effects on extreme precipitation in the coastal areas of southeast China

2021 ◽  
Author(s):  
Weikang Qian ◽  
Xun Sun
Keyword(s):  
Extreme Precipitation ◽  
Coastal Areas ◽  
Generalized Extreme Value Distribution ◽  
North India ◽  
Climate Indices ◽  
Human Society ◽  
Spatial Effects ◽  
Southeast China ◽  
Temporal Effects ◽  
Spatio Temporal

<p>Extreme precipitation is considered to be one of the natural disasters with greatest impact on human society, leading to floods and debris flows. To better understand the spatio-temporal effects on extreme precipitation, and to predict the intensity of extreme precipitation ahead in different return periods, this study focus on quantifying both climate and spatial effects on the intensity of extreme precipitation in coastal areas of southeast China, considering different weather system. A hierarchical Bayesian model with generalized extreme value distribution (GEV) is applied to maximum daily precipitation through 94 stations in study area from 1964 to 2013 in JAS. Tropical cyclone (TC) and non-TC influenced extreme precipitation are analyzed separately. Climate and spatial effects are introduced through regression models associating parameter values in GEV with different covariates, such as climate indices and distance to coastline. It was observed that SST anomaly in North Pacific, SLP anomaly above North India Ocean are found to be the main climate indices that influence extreme precipitation in coastal areas of southeast China. Using SST, we can predict the intensity of extreme precipitation in different return period at 6-month lag. Extreme precipitation was found to decrease as distance to coastline increase. In addition, different performances of extreme precipitation along with distance to coastline were found among various subregions and weather systems.</p>

Spatial effects on extreme precipitation in the coastal areas of southeast China

2020 ◽  
Author(s):  
Weikang Qian ◽  
Xun Sun
Keyword(s):  
Extreme Precipitation ◽  
Extreme Value Distribution ◽  
Coastal Areas ◽  
Generalized Extreme Value Distribution ◽  
Precipitation Event ◽  
Human Society ◽  
Spatial Effects ◽  
Southeast China ◽  
Spatial Factors ◽  
Daily Precipitation Data

<p>Extreme precipitation event, along with its secondary disasters, is one of the largest natural hazards leading to massive loss in human society. In the coastal areas of southeast china, tropical cyclones (TC) frequently visit the region with intensive precipitation in summer and autumn. Besides TC induced extreme precipitation, convectional precipitation is an alternative reason of extreme precipitation. This study investigated the spatial effects of the extreme precipitation during the raining season for both TC induced and non-TC induced extreme precipitation. The seasonal maximum daily precipitation data through 94 stations in southeast coastal areas of China from 1964 to 2013 were used. We developed a hierarchical Bayesian model with generalized extreme value distribution (GEV) to quantitatively assess the effects of spatial factors on the extreme precipitation. TC induced and non-TC induced extreme precipitation are modelled separately. It was found that the spatial factors that affect the TC induced and non-TC induced extreme precipitation are clearly different. For the TC induced extreme precipitation, the distance to the coastline has been found to be a significant spatial covariate that affects both the location and scale parameter of GEV across the whole areas, while spatial factors are diverse in different locations for non-TC induced extreme precipitation.</p>

scholarly journals Noise Disturbances and Calls for Police Service in València (Spain): A Logistic Model with Spatial and Temporal Effects

2019 ◽  
Vol 16 (16) ◽  
pp. 2815
Author(s):  
Lia Seguí ◽  
Adina Iftimi ◽  
Álvaro Briz-Redón ◽  
Lucía Martínez-Garay ◽  
Francisco Montes
Keyword(s):  
Logistic Model ◽  
Economic Status ◽  
Age Groups ◽  
Spatial Effects ◽  
Temporal Effects ◽  
Time Period ◽  
Local Residents ◽  
Noise Disturbances ◽  
Spatio Temporal ◽  
Noise Phenomenon

The purpose of this paper is to explore the presence of spatial and temporal effects on the calls for noise disturbance service reported to the Local Police of València (Spain) in the time period from 2014 to 2015, and investigate how some socio-demographic and environmental variables affect the noise phenomenon. The analysis is performed at the level of València’s boroughs. It has been carried out using a logistic model after dichotomization of the noise incidence variable. The spatial effects consider first- and second-order neighbors. The temporal effects are included in the model by means of one- and two-week temporal lags. Our model confirms the presence of strong spatio-temporal effects. We also find significant associations between noise incidence and specific age groups, socio-economic status, land uses, and recreational activities, among other variables. The results suggest that there is a problem of "social" noise in València that is not exclusively a consequence of coexistence between local residents. External factors such as the increasing number of people on the streets during weekend nights or during summer months severely increase the chances of expecting a noise incident.

scholarly journals Noise Disturbances and Calls for Police Service in València (Spain): a Logistic Model with Spatial and Temporal Effects

2019 ◽  
Author(s):  
Lia Seguí ◽  
Adina Iftimi ◽  
Álvaro Briz-Redón ◽  
Lucía Martínez-Garay ◽  
Francisco Montes
Keyword(s):  
Logistic Model ◽  
Economic Status ◽  
Age Groups ◽  
Spatial Effects ◽  
Temporal Effects ◽  
Time Period ◽  
Local Residents ◽  
Noise Disturbances ◽  
Spatio Temporal ◽  
Noise Phenomenon

The purpose of this paper is to explore the presence of spatial and temporal effects on the calls for noise disturbance service reported to the Local Police of València (Spain) in the time period from 2014 to 2015, and investigate how some socio-demographic and environmental variables affect the noise phenomenon. The analysis is performed at the level of València's boroughs. It has been carried out using a logistic model after dichotomization of the noise incidents variable. The spatial effects consider first and second order neighbours. The temporal effects are included in the model by means of one and two weeks temporal lags. Our model confirms the presence of strong spatio-temporal effects. We also find significant associations between noise incidence and specific age groups, socio-economic status, land uses and recreational activities, among other variables. The results suggest that there is a problem of ``social'' noise in València that is not exclusively a consequence of coexistence between local residents. External factors such as the increasing number of people on the streets during weekend nights or during summer months increase severely the chances of expecting a noise incident.

scholarly journals Quantifying the influence of natural climate variability on in situ measurements of seasonal total and extreme daily precipitation

2021 ◽  
Author(s):  
Mark D. Risser ◽  
Michael F. Wehner ◽  
John P. O’Brien ◽  
Christina M. Patricola ◽  
Travis A. O’Brien ◽  
...  
Keyword(s):  
Climate Variability ◽  
Extreme Precipitation ◽  
Southern Oscillation ◽  
Statistical Significance ◽  
In Situ Measurements ◽  
Climate Indices ◽  
Natural Climate Variability ◽  
Modes Of Variability ◽  
Natural Climate

AbstractWhile various studies explore the relationship between individual sources of climate variability and extreme precipitation, there is a need for improved understanding of how these physical phenomena simultaneously influence precipitation in the observational record across the contiguous United States. In this work, we introduce a single framework for characterizing the historical signal (anthropogenic forcing) and noise (natural variability) in seasonal mean and extreme precipitation. An important aspect of our analysis is that we simultaneously isolate the individual effects of seven modes of variability while explicitly controlling for joint inter-mode relationships. Our method utilizes a spatial statistical component that uses in situ measurements to resolve relationships to their native scales; furthermore, we use a data-driven procedure to robustly determine statistical significance. In Part I of this work we focus on natural climate variability: detection is mostly limited to DJF and SON for the modes of variability considered, with the El Niño/Southern Oscillation, the Pacific–North American pattern, and the North Atlantic Oscillation exhibiting the largest influence. Across all climate indices considered, the signals are larger and can be detected more clearly for seasonal total versus extreme precipitation. We are able to detect at least some significant relationships in all seasons in spite of extremely large (> 95%) background variability in both mean and extreme precipitation. Furthermore, we specifically quantify how the spatial aspect of our analysis reduces uncertainty and increases detection of statistical significance while also discovering results that quantify the complex interconnected relationships between climate drivers and seasonal precipitation.

scholarly journals Recognizing the Aggregation Characteristics of Extreme Precipitation Events Using Spatio-Temporal Scanning and the Local Spatial Autocorrelation Model

Atmosphere ◽  
2021 ◽  
Vol 12 (2) ◽  
pp. 218
Author(s):  
Changjun Wan ◽  
Changxiu Cheng ◽  
Sijing Ye ◽  
Shi Shen ◽  
Ting Zhang
Keyword(s):  
Spatial Autocorrelation ◽  
Extreme Precipitation ◽  
Eastern China ◽  
Temporal Aggregation ◽  
Global Changes ◽  
Percentile Method ◽  
Extreme Precipitation Events ◽  
Spatio Temporal ◽  
Precipitation Events ◽  
Local Spatial Autocorrelation

Precipitation is an essential climate variable in the hydrologic cycle. Its abnormal change would have a serious impact on the social economy, ecological development and life safety. In recent decades, many studies about extreme precipitation have been performed on spatio-temporal variation patterns under global changes; little research has been conducted on the regionality and persistence, which tend to be more destructive. This study defines extreme precipitation events by percentile method, then applies the spatio-temporal scanning model (STSM) and the local spatial autocorrelation model (LSAM) to explore the spatio-temporal aggregation characteristics of extreme precipitation, taking China in July as a case. The study result showed that the STSM with the LSAM can effectively detect the spatio-temporal accumulation areas. The extreme precipitation events of China in July 2016 have a significant spatio-temporal aggregation characteristic. From the spatial perspective, China’s summer extreme precipitation spatio-temporal clusters are mainly distributed in eastern China and northern China, such as Dongting Lake plain, the Circum-Bohai Sea region, Gansu, and Xinjiang. From the temporal perspective, the spatio-temporal clusters of extreme precipitation are mainly distributed in July, and its occurrence was delayed with an increase in latitude, except for in Xinjiang, where extreme precipitation events often take place earlier and persist longer.

scholarly journals Historical drought patterns over Canada and their teleconnections with large-scale climate signals

2018 ◽  
Vol 22 (6) ◽  
pp. 3105-3124 ◽  
Author(s):  
Zilefac Elvis Asong ◽  
Howard Simon Wheater ◽  
Barrie Bonsal ◽  
Saman Razavi ◽  
Sopan Kurkute
Keyword(s):  
Large Scale ◽  
Southern Oscillation ◽  
Low Frequency ◽  
Kendall Test ◽  
Wavelet Coherence ◽  
Climate Indices ◽  
Data Sets ◽  
Canadian Prairies ◽  
Agriculture Industry ◽  
Spatio Temporal

Abstract. Drought is a recurring extreme climate event and among the most costly natural disasters in the world. This is particularly true over Canada, where drought is both a frequent and damaging phenomenon with impacts on regional water resources, agriculture, industry, aquatic ecosystems, and health. However, nationwide drought assessments are currently lacking and impacted by limited ground-based observations. This study provides a comprehensive analysis of historical droughts over the whole of Canada, including the role of large-scale teleconnections. Drought events are characterized by the Standardized Precipitation Evapotranspiration Index (SPEI) over various temporal scales (1, 3, 6, and 12 consecutive months, 6 months from April to September, and 12 months from October to September) applied to different gridded monthly data sets for the period 1950–2013. The Mann–Kendall test, rotated empirical orthogonal function, continuous wavelet transform, and wavelet coherence analyses are used, respectively, to investigate the trend, spatio-temporal patterns, periodicity, and teleconnectivity of drought events. Results indicate that southern (northern) parts of the country experienced significant trends towards drier (wetter) conditions although substantial variability exists. Two spatially well-defined regions with different temporal evolution of droughts were identified – the Canadian Prairies and northern central Canada. The analyses also revealed the presence of a dominant periodicity of between 8 and 32 months in the Prairie region and between 8 and 40 months in the northern central region. These cycles of low-frequency variability are found to be associated principally with the Pacific–North American (PNA) and Multivariate El Niño/Southern Oscillation Index (MEI) relative to other considered large-scale climate indices. This study is the first of its kind to identify dominant periodicities in drought variability over the whole of Canada in terms of when the drought events occur, their duration, and how often they occur.

scholarly journals High-resolution analysis of 1 day extreme precipitation in Sicily

2015 ◽  
Vol 15 (10) ◽  
pp. 2347-2358 ◽  
Author(s):  
M. Maugeri ◽  
M. Brunetti ◽  
M. Garzoglio ◽  
C. Simolo
Keyword(s):  
High Resolution ◽  
Extreme Precipitation ◽  
Human Life ◽  
Extreme Value Distribution ◽  
Generalized Extreme Value Distribution ◽  
Return Level ◽  
Regional Frequency Analysis ◽  
Economic Losses ◽  
Data Set ◽  
High Quantiles

Abstract. Sicily, a major Mediterranean island, has experienced several exceptional precipitation episodes and floods during the last century, with serious damage to human life and the environment. Long-term, rational planning of urban development is indispensable to protect the population and to avoid huge economic losses in the future. This requires a thorough knowledge of the distributional features of extreme precipitation over the complex territory of Sicily. In this study, we perform a detailed investigation of observed 1 day precipitation extremes and their frequency distribution, based on a dense data set of high-quality, homogenized station records in 1921–2005. We estimate very high quantiles (return levels) corresponding to 10-, 50- and 100-year return periods, as predicted by a generalized extreme value distribution. Return level estimates are produced on a regular high-resolution grid (30 arcsec) using a variant of regional frequency analysis combined with regression techniques. Results clearly reflect the complexity of this region, and show the high vulnerability of its eastern and northeastern parts as those prone to the most intense and potentially damaging events.

scholarly journals Downscaling future precipitation extremes to urban hydrology scales using a spatio-temporal Neyman–Scott weather generator

2016 ◽  
Vol 20 (4) ◽  
pp. 1387-1403 ◽  
Author(s):  
Hjalte Jomo Danielsen Sørup ◽  
Ole Bøssing Christensen ◽  
Karsten Arnbjerg-Nielsen ◽  
Peter Steen Mikkelsen
Keyword(s):  
Climate Change ◽  
Time Series ◽  
Extreme Precipitation ◽  
Climate Models ◽  
Regional Climate ◽  
Weather Generator ◽  
Spatial Extent ◽  
Urban Hydrology ◽  
Precipitation Time Series ◽  
Spatio Temporal

Abstract. Spatio-temporal precipitation is modelled for urban application at 1 h temporal resolution on a 2 km grid using a spatio-temporal Neyman–Scott rectangular pulses weather generator (WG). Precipitation time series used as input to the WG are obtained from a network of 60 tipping-bucket rain gauges irregularly placed in a 40 km  ×  60 km model domain. The WG simulates precipitation time series that are comparable to the observations with respect to extreme precipitation statistics. The WG is used for downscaling climate change signals from regional climate models (RCMs) with spatial resolutions of 25 and 8 km, respectively. Six different RCM simulation pairs are used to perturb the WG with climate change signals resulting in six very different perturbation schemes. All perturbed WGs result in more extreme precipitation at the sub-daily to multi-daily level and these extremes exhibit a much more realistic spatial pattern than what is observed in RCM precipitation output. The WG seems to correlate increased extreme intensities with an increased spatial extent of the extremes meaning that the climate-change-perturbed extremes have a larger spatial extent than those of the present climate. Overall, the WG produces robust results and is seen as a reliable procedure for downscaling RCM precipitation output for use in urban hydrology.

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